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Xiaokai Bai

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Learning Better UAV-Based Cross-View Object Geo-Localization from Multi-Modal Prompts: MoP-UAV Benchmark and MoPT Framework

Xiaohan Zhang
Zhangkai Shen
Si-Yuan Cao
Xiaokai Bai
Yiming Li
Zheheng Han
Zhe Wu
Qi Ming

We present MoP-UAV, a new benchmark for UAV-based cross-view object geo-localization guided by multi-modal prompts. MoP-UAV supports fine-grained object-level cross-view localization under diverse prompt modalities, including natural language, bounding boxes, and click points. It offers potential for incorporating large foundation models like large language models (LLMs) and promotes the building of more flexible and intelligent UAV agents. Based on the benchmark, we propose MoPT, a multi-modal-prompt-guided tansformer that embeds prompts as token sequences and extract object location from UAV and satellite features via cross-attention. To enhance semantic consistency and performance, we further adopt a cross-view contrastive loss and propose a RefCOCOg-based pre-training strategy. Extensive experiments show that MoPT achieves robust localization under arbitrary prompt combinations. Notably, multi-modal-prompt training significantly boosts unimodal-prompt inference performance, highlighting the generalization benefits of multi-modal learning. MoPT trained with multi-modal prompts outperforms prior unimodal prompt works under the same setting.

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JBHI Journal 2026 Journal Article

Remote PPG Measurement Using a Synergistic Time-Frequency Network

Yiming Li
Qinglin He
Yihan Yang
Yuguang Chu
Yuanhui Hu
Zhe Wu
Xiaokai Bai
Xiaohan Zhang

Remote photoplethysmography (rPPG) aims to estimate the blood volume pulse (BVP) signal from facial videos. Existing rPPG approaches still suffer from limitations. We attribute this issue to two primary problems: (1) the reliance solely on time-domain processing that makes the signal susceptible to interference, and (2) the presence of a phase discrepancy between the supervision signal and the ground-truth PPG. To address these problems, we propose TFSNet, a novel time-frequency synergy network for rPPG signal estimation and heart rate prediction. Specifically, we leverage time-frequency fusion (TFF) module, which integrates frequency-domain information into the learning process to enrich the feature representations. Additionally, we introduce the amplitude-phase decoupling (APD) module, which apply phase compensation in frequency domain to mitigate the adverse effects of incorrect phase supervision. Extensive experiments demonstrate that TFSNet achieves state-of-the-art performance, significantly outperforming current approaches in both accuracy and robustness.

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IROS Conference 2025 Conference Paper

LGDD: Local-Global Synergistic Dual-Branch 3D Object Detection Using 4D Radar

Xiaokai Bai
Qin Yang
Zili Zhou
Fuyi Zhang
Zhe Wu
Si-Yuan Cao
Lianqing Zheng
Beinan Yu

4D millimeter-wave radar plays a pivotal role in autonomous driving due to its cost-effectiveness and robustness in adverse weather. However, the application of 4D radar point cloud in 3D perception tasks is hindered by its inherent sparsity and noise. To address these challenges, we propose LGDD, a novel local-global synergistic dual-branch 3D object detection framework using 4D radar. Specifically, we first introduce a point-based branch, which utilize a voxel-attended point feature extractor (VPE) to integrate semantic segmentation with cluster voting, thereby mitigating radar noise and extracting local-clustered instances features. Then, for the conventional pillar-based branch, we design a query-based feature pre-fusion (QFP) to address the sparsity and enhance global context representation. Additionally, we devise a proposal mask to filter out noisy points, enabling more focused clustering on regions of interest. Finally, we align the local instances with global context through semantics-geometry aware fusion (SGF) module to achieve comprehensive scene understanding. Extensive experiments demonstrate that LGDD achieves state-of-the-art performance on the public View-of-Delft and TJ4DRadSet datasets. Source code is available at https://github.com/shawnnnkb/LGDD.

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ICRA Conference 2025 Conference Paper

Structure-Aware Radar-Camera Depth Estimation

Fuyi Zhang
Zhu Yu 0001
Chunhao Li
Runmin Zhang
Xiaokai Bai
Zili Zhou
Si-Yuan Cao
Fang Wang

Radar has gained much attention in autonomous driving due to its accessibility and robustness. However, its standalone application for depth perception is constrained by issues of sparsity and noise. Radar-camera depth estimation offers a more promising complementary solution. Despite significant progress, current approaches fail to produce satisfactory dense depth maps, due to the unsatisfactory processing of the sparse and noisy radar data. They constrain the regions of interest for radar points in rigid rectangular regions, which may introduce unexpected errors and confusions. To address these issues, we develop a structure-aware strategy for radar depth enhancement, which provides more targeted regions of interest by leveraging the structural priors of RGB images. Furthermore, we design a Multi-Scale Structure Guided Network to enhance radar features and preserve detailed structures, achieving accurate and structure-detailed dense metric depth estimation. Building on these, we propose a structure-aware radar-camera depth estimation framework, named SA-RCD. Extensive experiments demonstrate that our SA-RCD achieves state-of-the-art performance on the nuScenes dataset. Our code will be available at https://github.com/FreyZhangYeh/SA-RCD.

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